Self-Organizing Map (SOM) is an unsupervised machine-learning algorithm that learns
a projection from a high-dimension input space into a low-dimension output space,
generally two-dimensional, to aid visualization.
The output space is modeled as a grid of neurons — a neural map — that
respond to a set of values from the input space [1]}.
The output model preserves the topology of the input space: any continuous changes in the
input data cause a continuous change on the neural map [2]}.
In other words, input values close in the original high-dimensional space
trigger neurons that are close in the low-dimensional projection [3]}.
